Search Results for author:
Found 7 papers, 1 papers with code
Human as AI Mentor: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving
In this paradigm, the human expert serves as a mentor to the AI agent.
A Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction
PERL contains a physics model and a residual learning model.
EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Prediction
Then, the planning information of the ego vehicle is encoded by both the planning graph and the subsequent planning-guided prediction module to reduce uncertainty in the trajectory prediction.
A Cooperation-Aware Lane Change Method for Automated Vehicles
Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV’s decision and surrounding vehicles’ interactive behaviors into constraints so as to avoid collisions during lane change.
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation
Real-time 6D object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality.
A Cooperation-Aware Lane Change Method for Autonomous Vehicles
Further, an evaluation is designed to make a decision on lane change, in which safety, efficiency and comfort are taken into consideration.
Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving
This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories.
